{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Continuous renal replacement therapy (CRRT)\n",
    "\n",
    "This notebook overviews the process of defining CRRT: a treatment used to dialyse or filter a patient's blood continuously. Key to CRRT is its lower speed compared to conventional dialysis: avoidance of rapid solute/fluid loss is suspected to be the main reason why CRRT tends to be tolerated better than intermittent hemodialysis.\n",
    "\n",
    "The primary aim of this notebook is to define the start and end times of CRRT for patients in the MIMIC-III database v1.4.\n",
    "\n",
    "A secondary aim of this notebook is to provide insight into how to extract clinical concepts from the MIMIC-III database.\n",
    "\n",
    "Many thanks to Sharon O'Donoghue for her invaluable advice in the creation of this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outline\n",
    "\n",
    "The main steps in defining a clinical concept in MIMIC-III are as follows:\n",
    "\n",
    "1. Identification of key terms and phrases which describe the concept\n",
    "2. Search for these terms in D_ITEMS (or D_LABITEMS if searching for a laboratory measurement)\n",
    "3. Extraction of the data from tables specified in the `LINKSTO` column of D_ITEMS\n",
    "4. Definition of the concept using rules applied to the data extracted\n",
    "5. Validation of the concepts by individual inspection and aggregate statistics\n",
    "\n",
    "This process is iterative and not as clear cut as the above - validation may lead you to redefine data extraction, and so on. Furthermore, in the case of MIMIC-III v1.4, this process must be repeated twice: once for Metavision, once for CareVue.\n",
    "\n",
    "## MetaVision vs. CareVue\n",
    "\n",
    "One issue in MIMIC-III is that it is a combination of two ICU database systems. As a result, concepts are split among different `ITEMID` values. For example, a patient's heart rate is a relatively simple concept to extract, however, if we look in the D_ITEMS table for labels matching 'heart rate', we find at least two `ITEMID`:\n",
    "\n",
    "itemid |          label          |  abbreviation   |  dbsource  |   linksto\n",
    "--------|-------------------------|-----------------|------------|-------------\n",
    "211 | Heart Rate              |                 | carevue    | chartevents\n",
    "220045 | Heart Rate              | HR              | metavision | chartevents\n",
    "\n",
    "Both these `ITEMID` values capture heart rate - but one is used for the CareVue database system (`dbsource = 'carevue'`) and one is used for the MetaVision database system (`dbsource = 'metavision'`). The data extraction step must be repeated twice: once for `dbsource = 'carevue'` and once for `dbsource = 'metavision'`. In general, it is recommended to extract data from MetaVision first, as the data is better structured and provides useful information for what data elements to include. For example, `ITEMID` values in MetaVision have abbrevations with each label - these abbreviations can then be used to search for data elements in CareVue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 0: import libraries, connect to the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import psycopg2\n",
    "from IPython.display import display, HTML # used to print out pretty pandas dataframes\n",
    "import matplotlib.dates as dates\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot') \n",
    "\n",
    "# specify user/password/where the database is\n",
    "sqluser = 'postgres'\n",
    "sqlpass = 'postgres'\n",
    "dbname = 'mimic'\n",
    "schema_name = 'mimiciii'\n",
    "host = 'localhost'\n",
    "\n",
    "query_schema = 'SET search_path to ' + schema_name + ';'\n",
    "\n",
    "# connect to the database\n",
    "con = psycopg2.connect(dbname=dbname, user=sqluser, password=sqlpass, host=host)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Identification of key terms\n",
    "\n",
    "We are interested in continuous renal replacement therapy (CRRT). First, we look for 'CRRT' in the database, isolating ourselves to metavision data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>itemid</th>\n",
       "      <th>label</th>\n",
       "      <th>category</th>\n",
       "      <th>linksto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>227290</td>\n",
       "      <td>CRRT mode</td>\n",
       "      <td>Dialysis</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>225436</td>\n",
       "      <td>CRRT Filter Change</td>\n",
       "      <td>Dialysis</td>\n",
       "      <td>procedureevents_mv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>227525</td>\n",
       "      <td>Calcium Gluconate (CRRT)</td>\n",
       "      <td>Medications</td>\n",
       "      <td>inputevents_mv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>225802</td>\n",
       "      <td>Dialysis - CRRT</td>\n",
       "      <td>Dialysis</td>\n",
       "      <td>procedureevents_mv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>227536</td>\n",
       "      <td>KCl (CRRT)</td>\n",
       "      <td>Medications</td>\n",
       "      <td>inputevents_mv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>225956</td>\n",
       "      <td>Reason for CRRT Filter Change</td>\n",
       "      <td>Dialysis</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   itemid                          label     category             linksto\n",
       "0  227290                      CRRT mode     Dialysis         chartevents\n",
       "1  225436             CRRT Filter Change     Dialysis  procedureevents_mv\n",
       "2  227525       Calcium Gluconate (CRRT)  Medications      inputevents_mv\n",
       "3  225802                Dialysis - CRRT     Dialysis  procedureevents_mv\n",
       "4  227536                     KCl (CRRT)  Medications      inputevents_mv\n",
       "5  225956  Reason for CRRT Filter Change     Dialysis         chartevents"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "query = query_schema + \"\"\"\n",
    "select itemid, label, category, linksto\n",
    "from d_items\n",
    "where dbsource = 'metavision'\n",
    "and lower(label) like '%crrt%'\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(query,con)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above gives us some hints to expand our initial search:\n",
    "\n",
    "* `category = 'Dialysis'`\n",
    "* `lower(label) like '%dialysis%'`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Extraction of `ITEMID`s from tables\n",
    "\n",
    "## Get list of `itemid` related to CRRT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>itemid</th>\n",
       "      <th>label</th>\n",
       "      <th>category</th>\n",
       "      <th>linksto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>225740</td>\n",
       "      <td>Dialysis Catheter Discontinued</td>\n",
       "      <td>Access Lines - Invasive</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>227357</td>\n",
       "      <td>Dialysis Catheter Dressing Occlusive</td>\n",
       "      <td>Access Lines - Invasive</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>225776</td>\n",
       "      <td>Dialysis Catheter Dressing Type</td>\n",
       "      <td>Access Lines - Invasive</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>226118</td>\n",
       "      <td>Dialysis Catheter placed in outside facility</td>\n",
       "      <td>Access Lines - Invasive</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>227753</td>\n",
       "      <td>Dialysis Catheter Placement Confirmed by X-ray</td>\n",
       "      <td>Access Lines - Invasive</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "select itemid, label, category, linksto\n",
    "from d_items di\n",
    "where dbsource = 'metavision'\n",
    "and (lower(label) like '%dialy%'\n",
    "or category = 'Dialysis'\n",
    "or lower(label) like '%crrt%'\n",
    ")\n",
    "order by linksto, category, label\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(query,con)\n",
    "\n",
    "HTML(df.head().to_html().replace('NaN', ''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manually label above `itemid`\n",
    "\n",
    "The above is a list of all the potential data elements which could be used to define CRRT. The next step is to identify the specific elements which can be used to define start/stop time. This process requires clinical expertise in the area.\n",
    "\n",
    "The following tables are a result of reviewing all `ITEMID` labels and flagging them as \"consider for further review\" or \"not relevant\".\n",
    "\n",
    "\n",
    "### Links to CHARTEVENTS\n",
    "\n",
    "itemid | label | category | linksto | Included/comment\n",
    "--- | --- | --- | --- | ---\n",
    "225740 | Dialysis Catheter Discontinued | Access Lines - Invasive | chartevents | No - access line\n",
    "227357 | Dialysis Catheter Dressing Occlusive | Access Lines - Invasive | chartevents | No - access line\n",
    "225776 | Dialysis Catheter Dressing Type | Access Lines - Invasive | chartevents | No - access line\n",
    "226118 | Dialysis Catheter placed in outside facility | Access Lines - Invasive | chartevents | No - access line\n",
    "227753 | Dialysis Catheter Placement Confirmed by X-ray | Access Lines - Invasive | chartevents | No - access line\n",
    "225323 | Dialysis Catheter Site Appear | Access Lines - Invasive | chartevents | No - access line\n",
    "225725 | Dialysis Catheter Tip Cultured | Access Lines - Invasive | chartevents | No - access line\n",
    "227124 | Dialysis Catheter Type | Access Lines - Invasive | chartevents | No - access line\n",
    "225126 | Dialysis patient | Adm History/FHPA | chartevents | No - admission information\n",
    "224149 | Access Pressure | Dialysis | chartevents | Yes - CRRT setting\n",
    "224404 | ART Lumen Volume | Dialysis | chartevents | Yes - CRRT setting\n",
    "224144 | Blood Flow (ml/min) | Dialysis | chartevents | Yes - CRRT setting\n",
    "228004 | Citrate (ACD-A) | Dialysis | chartevents | Yes - CRRT setting\n",
    "227290 | CRRT mode | Dialysis | chartevents | Yes - CRRT setting\n",
    "225183 | Current Goal | Dialysis | chartevents | Yes - CRRT setting\n",
    "225977 | Dialysate Fluid | Dialysis | chartevents | Yes - CRRT setting\n",
    "224154 | Dialysate Rate | Dialysis | chartevents | Yes - CRRT setting\n",
    "224135 | Dialysis Access Site | Dialysis | chartevents | No - access line\n",
    "225954 | Dialysis Access Type | Dialysis | chartevents | No - access line\n",
    "224139 | Dialysis Site Appearance | Dialysis | chartevents | No - access line\n",
    "225810 | Dwell Time (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "224151 | Effluent Pressure | Dialysis | chartevents | Yes - CRRT setting\n",
    "224150 | Filter Pressure | Dialysis | chartevents | Yes - CRRT setting\n",
    "226499 | Hemodialysis Output | Dialysis | chartevents | No - hemodialysis\n",
    "225958 | Heparin Concentration (units/mL) | Dialysis | chartevents | Yes - CRRT setting\n",
    "224145 | Heparin Dose (per hour) | Dialysis | chartevents | Yes - CRRT setting\n",
    "224191 | Hourly Patient Fluid Removal | Dialysis | chartevents | Yes - CRRT setting\n",
    "225952 | Medication Added #1 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "227638 | Medication Added #2 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "225959 | Medication Added Amount #1 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "227639 | Medication Added Amount #2 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "225961 | Medication Added Units #1 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "227640 | Medication Added Units #2 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "228005 | PBP (Prefilter) Replacement Rate | Dialysis | chartevents | Yes - CRRT setting\n",
    "225965 | Peritoneal Dialysis Catheter Status | Dialysis | chartevents | No - peritoneal dialysis\n",
    "225963 | Peritoneal Dialysis Catheter Type | Dialysis | chartevents | No - peritoneal dialysis\n",
    "225951 | Peritoneal Dialysis Fluid Appearance | Dialysis | chartevents | No - peritoneal dialysis\n",
    "228006 | Post Filter Replacement Rate | Dialysis | chartevents | Yes - CRRT setting\n",
    "225956 | Reason for CRRT Filter Change | Dialysis | chartevents | Yes - CRRT setting\n",
    "225976 | Replacement Fluid | Dialysis | chartevents | Yes - CRRT setting\n",
    "224153 | Replacement Rate | Dialysis | chartevents | Yes - CRRT setting\n",
    "224152 | Return Pressure | Dialysis | chartevents | Yes - CRRT setting\n",
    "225953 | Solution (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "224146 | System Integrity | Dialysis | chartevents | Yes - CRRT setting\n",
    "226457 | Ultrafiltrate Output | Dialysis | chartevents | Yes - CRRT setting\n",
    "224406 | VEN Lumen Volume | Dialysis | chartevents | Yes - CRRT setting\n",
    "225806 | Volume In (PD) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "227438 | Volume not removed | Dialysis | chartevents | No - peritoneal dialysis\n",
    "225807 | Volume Out (PD) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "\n",
    "### Links to DATETIMEEVENTS\n",
    "\n",
    "itemid | label | category | linksto | Included/comment\n",
    "--- | --- | --- | --- | ---\n",
    "225318 | Dialysis Catheter Cap Change | Access Lines - Invasive | datetimeevents | No - access lines\n",
    "225319 | Dialysis Catheter Change over Wire Date | Access Lines - Invasive | datetimeevents | No - access lines\n",
    "225321 | Dialysis Catheter Dressing Change | Access Lines - Invasive | datetimeevents | No - access lines\n",
    "225322 | Dialysis Catheter Insertion Date | Access Lines - Invasive | datetimeevents | No - access lines\n",
    "225324 | Dialysis CatheterTubing Change | Access Lines - Invasive | datetimeevents | No - access lines\n",
    "225128 | Last dialysis | Adm History/FHPA | datetimeevents | No - admission information\n",
    "\n",
    "### Links to INPUTEVENTS_MV\n",
    "\n",
    "itemid | label | category | linksto | Included/comment\n",
    "--- | --- | --- | --- | ---\n",
    "227525 | Calcium Gluconate (CRRT) | Medications | inputevents_mv | Yes - CRRT setting\n",
    "227536 | KCl (CRRT) | Medications | inputevents_mv | Yes - CRRT setting\n",
    "\n",
    "### Links to PROCEDUREEVENTS_MV\n",
    "\n",
    "itemid | label | category | linksto | Included/comment\n",
    "--- | --- | --- | --- | ---\n",
    "225441 | Hemodialysis | 4-Procedures | procedureevents_mv | No - hemodialysis\n",
    "224270 | Dialysis Catheter | Access Lines - Invasive | procedureevents_mv | No - access lines\n",
    "225436 | CRRT Filter Change | Dialysis | procedureevents_mv | Yes - CRRT setting\n",
    "225802 | Dialysis - CRRT | Dialysis | procedureevents_mv | Yes - CRRT setting\n",
    "225803 | Dialysis - CVVHD | Dialysis | procedureevents_mv | Yes - CRRT setting\n",
    "225809 | Dialysis - CVVHDF | Dialysis | procedureevents_mv | Yes - CRRT setting\n",
    "225955 | Dialysis - SCUF | Dialysis | procedureevents_mv | Yes - CRRT setting\n",
    "225805 | Peritoneal Dialysis | Dialysis | procedureevents_mv | No - peritoneal dialysis\n",
    "\n",
    "## Reasons for inclusion/exclusion\n",
    "\n",
    "* CRRT Setting - yes (included) - these settings are only documented when a patient is receiving CRRT.\n",
    "* Access lines- no (excluded) - these ITEMIDs are not included as the presence of an access line does *not* guarantee that CRRT is being delivered. While having an access line is a requirement of performing CRRT, these lines are present even when a patient is not actively being hemodialysed.\n",
    "* Peritoneal dialysis - no (excluded) - Peritoneal dialysis is a different form of dialysis, and is not CRRT\n",
    "* Hemodialysis - no (excluded) - Similar as above, hemodialysis is a different form of dialysis and is not CRRT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define rules based upon ITEMIDs\n",
    "\n",
    "Above, we acquired a list of `itemid` which we determined to be related to administration of CRRT. The next step is to determine *how* these `itemid` relate to CRRT: do they indicate it is started, stopped, continuing, or something else.\n",
    "\n",
    "We will evaluate `itemid` from three tables, in turn: CHARTEVENTS, INPUTEVENTS_MV, and PROCEDUREEVENTS_MV. Note that the \\_MV subscript indicates that the table only has data from MetaVision (half the patients), while \\_CV indicates the table only has data from CareVue (the other half of patients). Note that after we extract data from MetaVision patients, we will repeat this exercise for CareVue patients.\n",
    "\n",
    "### table 1 of 3: `itemid` from CHARTEVENTS\n",
    "\n",
    "These are the included CRRT settings in CHARTEVENTS:\n",
    "\n",
    "itemid |              label               | param_type \n",
    "--------|----------------------------------|------------\n",
    "224144 | Blood Flow (ml/min)              | Numeric\n",
    "224145 | Heparin Dose (per hour)          | Numeric\n",
    "224146 | System Integrity                 | Text\n",
    "224149 | Access Pressure                  | Numeric\n",
    "224150 | Filter Pressure                  | Numeric\n",
    "224151 | Effluent Pressure                | Numeric\n",
    "224152 | Return Pressure                  | Numeric\n",
    "224153 | Replacement Rate                 | Numeric\n",
    "224154 | Dialysate Rate                   | Numeric\n",
    "224191 | Hourly Patient Fluid Removal     | Numeric\n",
    "224404 | ART Lumen Volume                 | Numeric\n",
    "224406 | VEN Lumen Volume                 | Numeric\n",
    "225183 | Current Goal                     | Numeric\n",
    "225956 | Reason for CRRT Filter Change    | Text\n",
    "225958 | Heparin Concentration (units/mL) | Text\n",
    "225976 | Replacement Fluid                | Text\n",
    "225977 | Dialysate Fluid                  | Text\n",
    "226457 | Ultrafiltrate Output             | Numeric\n",
    "227290 | CRRT mode                        | Text\n",
    "228004 | Citrate (ACD-A)                  | Numeric\n",
    "228005 | PBP (Prefilter) Replacement Rate | Numeric\n",
    "228006 | Post Filter Replacement Rate     | Numeric\n",
    "\n",
    "First, we examine the numeric fields. These fields are the core CRRT settings which, according to clinical advice, should be documented hourly for patients actively on CRRT:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>icustay_id</th>\n",
       "      <th>label</th>\n",
       "      <th>charttime</th>\n",
       "      <th>value</th>\n",
       "      <th>valueuom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>246866</td>\n",
       "      <td>ART Lumen Volume</td>\n",
       "      <td>2161-12-11 20:00:00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>mL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>246866</td>\n",
       "      <td>VEN Lumen Volume</td>\n",
       "      <td>2161-12-11 20:00:00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>mL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>246866</td>\n",
       "      <td>Access Pressure</td>\n",
       "      <td>2161-12-11 23:43:00</td>\n",
       "      <td>-87</td>\n",
       "      <td>mmHg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>246866</td>\n",
       "      <td>Blood Flow (ml/min)</td>\n",
       "      <td>2161-12-11 23:43:00</td>\n",
       "      <td>200</td>\n",
       "      <td>ml/min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>246866</td>\n",
       "      <td>Citrate (ACD-A)</td>\n",
       "      <td>2161-12-11 23:43:00</td>\n",
       "      <td>0</td>\n",
       "      <td>ml/hr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "select\n",
    "  ce.icustay_id, di.label, ce.charttime\n",
    "  , ce.value\n",
    "  , ce.valueuom\n",
    "from chartevents ce\n",
    "inner join d_items di\n",
    "on ce.itemid = di.itemid\n",
    "where ce.icustay_id = 246866\n",
    "and ce.itemid in\n",
    "(\n",
    "        224404, -- | ART Lumen Volume\n",
    "        224406, -- | VEN Lumen Volume\n",
    "        228004, -- | Citrate (ACD-A)\n",
    "        224145, -- | Heparin Dose (per hour)\n",
    "        225183, -- | Current Goal\n",
    "        224149, -- | Access Pressure\n",
    "        224144, -- | Blood Flow (ml/min)\n",
    "        224154, -- | Dialysate Rate\n",
    "        224151, -- | Effluent Pressure\n",
    "        224150, -- | Filter Pressure\n",
    "        224191, -- | Hourly Patient Fluid Removal\n",
    "        228005, -- | PBP (Prefilter) Replacement Rate\n",
    "        228006, -- | Post Filter Replacement Rate\n",
    "        224153, -- | Replacement Rate\n",
    "        224152, -- | Return Pressure\n",
    "        226457  -- | Ultrafiltrate Output\n",
    ")\n",
    "order by ce.icustay_id, ce.charttime, di.label;\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(query,con)\n",
    "\n",
    "HTML(df.head().to_html().replace('NaN', ''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we can see that `ART Lumen Volume and VEN Lumen Volume` are documented at a drastically different time than the other settings. Upon discussion with a clinical expert, they confirmed that this is expected, as these volumes indicate settings to keep open the line and are not directly relevant to the administration of CRRT - at best they are superfluous and at worst they can mislead the start/stop times. As a result `ART Lumen Volume` and `VEN Lumen Volume` are excluded. This leaves us with the final set of `ITEMID`s:\n",
    "\n",
    "```sql\n",
    "224149, -- Access Pressure\n",
    "224144, -- Blood Flow (ml/min)\n",
    "228004, -- Citrate (ACD-A)\n",
    "225183, -- Current Goal\n",
    "224154, -- Dialysate Rate\n",
    "224151, -- Effluent Pressure\n",
    "224150, -- Filter Pressure\n",
    "224145, -- Heparin Dose (per hour)\n",
    "224191, -- Hourly Patient Fluid Removal\n",
    "228005, -- PBP (Prefilter) Replacement Rate\n",
    "228006, -- Post Filter Replacement Rate\n",
    "224153, -- Replacement Rate\n",
    "224152, -- Return Pressure\n",
    "226457  -- Ultrafiltrate Output\n",
    "```\n",
    "\n",
    "The next step is to examine the remaining text based `ITEMID`:\n",
    "\n",
    "\n",
    "\n",
    "itemid |              label               | param_type \n",
    "--------|----------------------------------|------------\n",
    "224146 | System Integrity                 | Text\n",
    "225956 | Reason for CRRT Filter Change    | Text\n",
    "225958 | Heparin Concentration (units/mL) | Text\n",
    "225976 | Replacement Fluid                | Text\n",
    "225977 | Dialysate Fluid                  | Text\n",
    "227290 | CRRT mode                        | Text\n",
    "\n",
    "We define a helper function which prints out the number of observations for a given `itemid`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_itemid_info(con, itemid):\n",
    "    # get name of itemid\n",
    "    query = query_schema + \"\"\"\n",
    "    select label\n",
    "    from d_items\n",
    "    where itemid = \"\"\" + str(itemid)\n",
    "    \n",
    "    df = pd.read_sql_query(query,con)\n",
    "    print('Values for {} - {}...'.format(itemid, df['label'][0]))\n",
    "    \n",
    "    \n",
    "    query = query_schema + \"\"\"\n",
    "    select value\n",
    "    , count(distinct icustay_id) as number_of_patients\n",
    "    , count(icustay_id) as number_of_observations\n",
    "    from chartevents\n",
    "    where itemid = \"\"\" + str(itemid) + \"\"\"    \n",
    "    group by value\n",
    "    order by value\n",
    "    \"\"\"\n",
    "    df = pd.read_sql_query(query,con)\n",
    "    display(HTML(df.to_html().replace('NaN', '')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 224146 - System Integrity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for 224146 - System Integrity...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Active</td>\n",
       "      <td>539</td>\n",
       "      <td>48072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clots Increasing</td>\n",
       "      <td>245</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clots Present</td>\n",
       "      <td>427</td>\n",
       "      <td>16836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clotted</td>\n",
       "      <td>233</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Discontinued</td>\n",
       "      <td>339</td>\n",
       "      <td>771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Line pressure inconsistent</td>\n",
       "      <td>127</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Filter</td>\n",
       "      <td>357</td>\n",
       "      <td>1040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>No Clot Present</td>\n",
       "      <td>275</td>\n",
       "      <td>2615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Recirculating</td>\n",
       "      <td>172</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Reinitiated</td>\n",
       "      <td>336</td>\n",
       "      <td>1207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_itemid_info(con, 224146)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In discussion with a clinical expert, each of these settings indicate different stages of the CRRT treatment. We can simplify them into three modes: started, stopped, or active. Since active implies that the CRRT is running, the first active event could also be a start time, therefore we call it \"active/started\". Here we list the manually curated mapping:\n",
    "\n",
    "value | count | interpretation\n",
    "--- | --- | ---\n",
    "Active                     |   539 | CRRT active/started\n",
    "Clots Increasing           |   245 | CRRT active/started\n",
    "Clots Present              |   427 | CRRT active/started\n",
    "Clotted                    |   233 | CRRT **stopped**\n",
    "Discontinued               |   339 | CRRT **stopped**\n",
    "Line pressure inconsistent |   127 | CRRT active/started\n",
    "New Filter                 |   357 | CRRT **started**\n",
    "No Clot Present            |   275 | CRRT active/started\n",
    "Recirculating              |   172 | CRRT **stopped**\n",
    "Reinitiated                |   336 | CRRT **started**\n",
    "\n",
    "Later on we will code special rules to incorporate this `itemid`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 225956 - Reason for CRRT Filter Change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for 225956 - Reason for CRRT Filter Change...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Clotted</td>\n",
       "      <td>50</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Line changed</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Procedure</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_itemid_info(con, 225956)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is a **stop** time as the filter needed to be changed at this time. Any subsequent CRRT would be a restart of CRRT - and not a continuation of an ongoing CRRT session."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 225958 - Heparin Concentration (units/mL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for 225958 - Heparin Concentration (units/mL)...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>16</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>41</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Not applicable</td>\n",
       "      <td>120</td>\n",
       "      <td>8796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_itemid_info(con, 225958)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is a normal setting and can be combined with the numeric fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 225976 - Replacement Fluid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for 225976 - Replacement Fluid...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Normal Saline 0.9%</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prismasate K0</td>\n",
       "      <td>78</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prismasate K2</td>\n",
       "      <td>459</td>\n",
       "      <td>27603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prismasate K4</td>\n",
       "      <td>387</td>\n",
       "      <td>30872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sodium Bicarb 150/D5W</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sodium Bicarb 75/0.45NS</td>\n",
       "      <td>6</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_itemid_info(con, 225976)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is a normal setting and can be combined with the numeric fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 225977 - Dialysate Fluid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for 225977 - Dialysate Fluid...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>97</td>\n",
       "      <td>6025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Normal Saline</td>\n",
       "      <td>32</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prismasate K0</td>\n",
       "      <td>89</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prismasate K2</td>\n",
       "      <td>438</td>\n",
       "      <td>24271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prismasate K4</td>\n",
       "      <td>357</td>\n",
       "      <td>27320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_itemid_info(con, 225977)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is a normal setting and can be combined with the numeric fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 227290 - CRRT mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for 227290 - CRRT mode...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CVVH</td>\n",
       "      <td>40</td>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CVVHD</td>\n",
       "      <td>24</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CVVHDF</td>\n",
       "      <td>498</td>\n",
       "      <td>25533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SCUF</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_itemid_info(con, 227290)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While all of this looks good, it's feasible that the documentation of the CRRT mode is not done directly concurrent to the actual administration of CRRT. We thus investigate whether CRRT mode is available for all patients with a CRRT setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_icustay_id</th>\n",
       "      <th>num_with_mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>784</td>\n",
       "      <td>533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examining CRRT mode\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as \n",
    "(\n",
    "select icustay_id,\n",
    "    max(case when itemid = 227290 then 1 else 0 end) as HasMode\n",
    "from chartevents ce\n",
    "where itemid in\n",
    "(\n",
    "        227290, --  CRRT mode\n",
    "        228004, --  Citrate (ACD-A)\n",
    "        225958, --  Heparin Concentration (units/mL)\n",
    "        224145, --  Heparin Dose (per hour)\n",
    "        225183, --  Current Goal -- always there\n",
    "        224149, --  Access Pressure\n",
    "        224144, --  Blood Flow (ml/min)\n",
    "        225977, --  Dialysate Fluid\n",
    "        224154, --  Dialysate Rate\n",
    "        224151, --  Effluent Pressure\n",
    "        224150, --  Filter Pressure\n",
    "        224191, --  Hourly Patient Fluid Removal\n",
    "        228005, --  PBP (Prefilter) Replacement Rate\n",
    "        228006, --  Post Filter Replacement Rate\n",
    "        225976, --  Replacement Fluid\n",
    "        224153, --  Replacement Rate\n",
    "        224152, --  Return Pressure\n",
    "        226457  --  Ultrafiltrate Output\n",
    ")\n",
    "group by icustay_id\n",
    ")\n",
    "select count(icustay_id) as Num_ICUSTAY_ID\n",
    ", sum(hasmode) as Num_With_Mode\n",
    "from t1\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(query,con)\n",
    "\n",
    "HTML(df.to_html().replace('NaN', ''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take this analysis a bit further and ask: is CRRT mode is present when *none* of the other settings are present?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numobs</th>\n",
       "      <th>both</th>\n",
       "      <th>onlycrrtmode</th>\n",
       "      <th>nocrrtmode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81162</td>\n",
       "      <td>27446</td>\n",
       "      <td>1</td>\n",
       "      <td>53778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "with t1 as \n",
    "(\n",
    "select icustay_id, charttime\n",
    "    , max(case when itemid = 227290 then 1 else 0 end) as HasCRRTMode\n",
    "    , max(case when itemid != 227290 then 1 else 0 end) as OtherITEMID\n",
    "from chartevents ce\n",
    "where itemid in\n",
    "(\n",
    "        227290, --  CRRT mode\n",
    "        228004, --  Citrate (ACD-A)\n",
    "        225958, --  Heparin Concentration (units/mL)\n",
    "        224145, --  Heparin Dose (per hour)\n",
    "        225183, --  Current Goal -- always there\n",
    "        224149, --  Access Pressure\n",
    "        224144, --  Blood Flow (ml/min)\n",
    "        225977, --  Dialysate Fluid\n",
    "        224154, --  Dialysate Rate\n",
    "        224151, --  Effluent Pressure\n",
    "        224150, --  Filter Pressure\n",
    "        224191, --  Hourly Patient Fluid Removal\n",
    "        228005, --  PBP (Prefilter) Replacement Rate\n",
    "        228006, --  Post Filter Replacement Rate\n",
    "        225976, --  Replacement Fluid\n",
    "        224153, --  Replacement Rate\n",
    "        224152, --  Return Pressure\n",
    "        226457  --  Ultrafiltrate Output\n",
    ")\n",
    "group by icustay_id, charttime\n",
    ")\n",
    "select count(icustay_id) as NumObs\n",
    ", sum(case when HasCRRTMode = 1 and OtherITEMID = 1 then 1 else 0 end) as Both\n",
    ", sum(case when HasCRRTMode = 1 and OtherITEMID = 0 then 1 else 0 end) as OnlyCRRTMode\n",
    ", sum(case when HasCRRTMode = 0 and OtherITEMID = 1 then 1 else 0 end) as NoCRRTMode\n",
    "from t1\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(query,con)\n",
    "\n",
    "HTML(df.to_html().replace('NaN', ''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As CRRT mode is relatively redundant, doesn't necessarily indicate CRRT is being actively performed, and documentation for it is not 100% compliant, we exclude it from the list of `ITEMID`.\n",
    "\n",
    "### CHARTEVENTS wrap up\n",
    "\n",
    "The following is the final set of `ITEMID` from CHARTEVENTS which indicate CRRT is started/ongoing:\n",
    "\n",
    "```sql\n",
    "224149, -- Access Pressure\n",
    "224144, -- Blood Flow (ml/min)\n",
    "228004, -- Citrate (ACD-A)\n",
    "225183, -- Current Goal\n",
    "225977, -- Dialysate Fluid\n",
    "224154, -- Dialysate Rate\n",
    "224151, -- Effluent Pressure\n",
    "224150, -- Filter Pressure\n",
    "225958, -- Heparin Concentration (units/mL)\n",
    "224145, -- Heparin Dose (per hour)\n",
    "224191, -- Hourly Patient Fluid Removal\n",
    "228005, -- PBP (Prefilter) Replacement Rate\n",
    "228006, -- Post Filter Replacement Rate\n",
    "225976, -- Replacement Fluid\n",
    "224153, -- Replacement Rate\n",
    "224152, -- Return Pressure\n",
    "226457  -- Ultrafiltrate Output\n",
    "```\n",
    "\n",
    "The following `ITEMID` are the final set which indicate CRRT is started/stopped/ongoing (i.e. require special rules):\n",
    "\n",
    "```sql\n",
    "224146, -- System Integrity\n",
    "225956  -- Reason for CRRT Filter Change\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### table 2 of 3: INPUTEVENTS_MV\n",
    "\n",
    "The following is the final set of ITEMID from INPUTEVENTS_MV:\n",
    "\n",
    "```sql\n",
    "227525,-- Calcium Gluconate (CRRT)\n",
    "227536 -- KCl (CRRT)\n",
    "```\n",
    "\n",
    "No special examination is required for these fields - they are guaranteed to be CRRT (as verified by a clinician) - we can use these to indicate that CRRT is active/started."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### table 3 of 3: PROCEDUREEVENTS_MV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following are the set of `itemid` from above related to PROCEDUREEVENTS_MV:\n",
    "\n",
    "itemid | label\n",
    "--- | ---\n",
    "225436 | CRRT Filter Change\n",
    "225802 | Dialysis - CRRT\n",
    "225803 | Dialysis - CVVHD\n",
    "225809 | Dialysis - CVVHDF\n",
    "225955 | Dialysis - SCUF\n",
    "\n",
    "The only contentious `ITEMID` is 225436 (CRRT Filter Change). This `ITEMID` indicates a break from CRRT, and it reinitiates at the end of this change. While in principle this could be used as an end time, documentation on it is not 100%, and as recommended by staff it's easier to ignore this and use the filter change field from CHARTEVENTS to define the end of CRRT events.\n",
    "\n",
    "The final set of `ITEMID` used for CRRT are:\n",
    "\n",
    "```sql\n",
    "225802, -- Dialysis - CRRT\n",
    "225803, -- Dialysis - CVVHD\n",
    "225809, -- Dialysis - CVVHDF\n",
    "225955  -- Dialysis - SCUF\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: definition of concept using rules\n",
    "\n",
    "Let's review the goal of this notebook. We would like to define the duration of CRRT for each patient. Concretely, this means we must define, for each `ICUSTAY_ID`:\n",
    "\n",
    "* a `STARTTIME`\n",
    "* an `ENDTIME`\n",
    "\n",
    "As CRRT can be started/stopped throughout a patient's stay, there may be multiple `STARTTIME` and `ENDTIME` for a single `ICUSTAY_ID` - but they should not overlap.\n",
    "\n",
    "Recall that CHARTEVENTS stores data at charted times (`CHARTTIME`), and as a result the settings are stored at a single point in time. For CHARTEVENTS, the main task thus becomes converting a series of `CHARTTIME` into pairs of `STARTTIME` and `ENDTIME`. Intuitively this can be done by looking for consecutive settings each hour, and combining these into a single CRRT event. The first observed `CHARTTIME` becomes the `STARTTIME`, and the last observed `CHARTTIME` becomes the `ENDTIME`. However, CHARTEVENTS is not the only source of data. To improve the accuracy of our calculation, we also include data from INPUTEVENTS_MV and PROCEDUREEVENTS_MV. For INPUTEVENTS_MV, this does not complicate things too much. Each observation in INPUTEVENTS_MV is also stored at a single `CHARTTIME`, and so we simply need to combine this table with CHARTEVENTS before proceeding (likely by using the SQL `UNION` command).\n",
    "\n",
    "PROCEDUREEVENTS_MV is more complicated as it actually stores data with a `STARTTIME` and an `ENDTIME` column already. We need to merge the extracted data from CHARTEVENTS/INPUTEVENTS_MV with this already nicely formatted data from PROCEDUREEVENTS_MV.\n",
    "\n",
    "With the task laid out, let's get started. We will:\n",
    "\n",
    "1. Aggregate INPUTEVENTS_MV into durations\n",
    "2. Convert CHARTEVENTS into durations\n",
    "2. Compare these durations with PROCEDUREVENTS_MV and decide on a rule for merging the two\n",
    "3. Merge PROCEDUREEVENTS_MV with INPUTEVENTS_MV/CHARTEVENTS for a final durations table for Metavision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define the example ICUSTAY_ID for the below code\n",
    "# originally, this was 246866 - if changed, the interpretation provided will no longer make sense\n",
    "query_where_clause = \"and icustay_id = 246866\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure we don't display data we don't have to, we define a function which: (i) doesn't display icustay_id, and (ii) simplifies the date by removing the month/year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def display_df(df):\n",
    "    col = [x for x in df.columns if x != 'icustay_id']\n",
    "    df_tmp = df[col].copy()\n",
    "    for c in df_tmp.columns:\n",
    "        if '[ns]' in str(df_tmp[c].dtype):\n",
    "            df_tmp[c] = df_tmp[c].dt.strftime('Day %d, %H:%M')\n",
    "    \n",
    "    display(HTML(df_tmp.to_html().replace('NaN', '')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregating INPUTEVENTS_MV\n",
    "\n",
    "First, let's look at INPUTEVENTS_MV. Each entry is stored with a `starttime` and an `endtime`. Note we have to exclude `statusdescription = 'Rewritten'` as these are undelivered medications which have been rewritten (useful for auditing purposes but does not give you information about drugs delivered to the patient)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from INPUTEVENTS for one patient with KCl...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>linkorderid</th>\n",
       "      <th>orderid</th>\n",
       "      <th>label</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>rate</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>statusdescription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8522257</td>\n",
       "      <td>8522257</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 21:30</td>\n",
       "      <td>Day 12, 02:30</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9370484</td>\n",
       "      <td>9370484</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>10.002273</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3507252</td>\n",
       "      <td>3507252</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>9.997713</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9525961</td>\n",
       "      <td>9525961</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>10.285715</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7118985</td>\n",
       "      <td>7118985</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>10.112360</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5395095</td>\n",
       "      <td>5395095</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8065541</td>\n",
       "      <td>8065541</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>10.169492</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5758899</td>\n",
       "      <td>5758899</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7157126</td>\n",
       "      <td>7157126</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Paused</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7157126</td>\n",
       "      <td>3853798</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>9.997634</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7157126</td>\n",
       "      <td>6292957</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>6.190549</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>271343</td>\n",
       "      <td>271343</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>6.040268</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9407942</td>\n",
       "      <td>9407942</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>6.006060</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9182119</td>\n",
       "      <td>9182119</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>Day 13, 16:29</td>\n",
       "      <td>6.005904</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9720623</td>\n",
       "      <td>9720623</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2194578</td>\n",
       "      <td>2194578</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 15:28</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7567525</td>\n",
       "      <td>7567525</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7567525</td>\n",
       "      <td>4605649</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>4.007380</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7567525</td>\n",
       "      <td>5699592</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>5.871428</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8743715</td>\n",
       "      <td>8743715</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>5.905264</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4080709</td>\n",
       "      <td>4080709</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>5.969388</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4644782</td>\n",
       "      <td>4644782</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>5.906736</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>741589</td>\n",
       "      <td>741589</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>5.905759</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6190220</td>\n",
       "      <td>6190220</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>5.904762</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1921010</td>\n",
       "      <td>1921010</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3011912</td>\n",
       "      <td>3011912</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>5.906250</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1107318</td>\n",
       "      <td>1107318</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>5.907693</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>609665</td>\n",
       "      <td>609665</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4995198</td>\n",
       "      <td>4995198</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4667423</td>\n",
       "      <td>4667423</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4802077</td>\n",
       "      <td>4802077</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>8427007</td>\n",
       "      <td>8427007</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>Day 16, 16:04</td>\n",
       "      <td>5.905613</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Durations from INPUTEVENTS for one patient with KCl...\")\n",
    "query = query_schema + \"\"\"\n",
    "select \n",
    "    linkorderid\n",
    "  , orderid\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime, endtime\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  --227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    "order by starttime, endtime\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normally `linkorderid` links together administrations which are consecutive but may have changes in rate, but from the above we can note that `linkorderid` seems to rarely group entries.  Rows 8-10 and 16-18 are grouped (i.e. they are sequential administrations where the rate may or may not have changed), but many aren't *even though* they occur sequentially. We'd like to merge together sequential events to simplify the durations - and it appears we can greatly simplify this data by merging two rows if `endtime(row-1) == starttime(row)`.\n",
    "\n",
    "We can do this in three steps:\n",
    "\n",
    "1. Create a binary flag that indicates when new \"events\" occur, where an \"event\" is defined as a continuous segment of administration, i.e. the binary flag is 1 if the row does not immediately follow the previous row, and 0 if the row does immediately follow the previous row\n",
    "2. Aggregate this binary flag so each individual event is assigned a unique integer (i.e. create a partition over these events)\n",
    "3. Create an integer to identify the last row in the event (so we can get useful information from this row)\n",
    "4. Group the data based off the partition to result in a single `starttime` and `endtime` for each continguous medication administration\n",
    "\n",
    "Now we'll go through the code for doing this step by step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: create a binary flag for new events\n",
    "\n",
    "```sql\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  --227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    "```\n",
    "\n",
    "This selects data from INPUTEVENTS_MV for just KCl using a single patient specified by the `query_where_clause` (this is so it can act as an example - you can omit the single patient and it will work on all the data).\n",
    "\n",
    "The key code block is here:\n",
    "\n",
    "```sql\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "```\n",
    "\n",
    "This creates a boolean flag which is 1 every time the current `starttime` is not equal to the previous `endtime`, i.e. it marks new \"events\". We can see it in action here:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from INPUTEVENTS_MV, new events noted with time_partition...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>endtime_lag</th>\n",
       "      <th>new_event_flag</th>\n",
       "      <th>rate</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>statusdescription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 21:30</td>\n",
       "      <td>Day 12, 02:30</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>Day 12, 02:30</td>\n",
       "      <td>1</td>\n",
       "      <td>10.002273</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>0</td>\n",
       "      <td>9.997713</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>0</td>\n",
       "      <td>10.285715</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>0</td>\n",
       "      <td>10.112360</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>0</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>0</td>\n",
       "      <td>10.169492</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>0</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Paused</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>1</td>\n",
       "      <td>9.997634</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>0</td>\n",
       "      <td>6.190549</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>0</td>\n",
       "      <td>6.040268</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>0</td>\n",
       "      <td>6.006060</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>Day 13, 16:29</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>0</td>\n",
       "      <td>6.005904</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>Day 13, 16:29</td>\n",
       "      <td>1</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 15:28</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>1</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>0</td>\n",
       "      <td>4.007380</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>0</td>\n",
       "      <td>5.871428</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>0</td>\n",
       "      <td>5.905264</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>0</td>\n",
       "      <td>5.969388</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>0</td>\n",
       "      <td>5.906736</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>0</td>\n",
       "      <td>5.905759</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>0</td>\n",
       "      <td>5.904762</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>0</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>0</td>\n",
       "      <td>5.906250</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>0</td>\n",
       "      <td>5.907693</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>0</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>0</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>0</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>0</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>Day 16, 16:04</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>0</td>\n",
       "      <td>5.905613</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Durations from INPUTEVENTS_MV, new events noted with time_partition...\")\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select \n",
    "  icustay_id\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime, endtime\n",
    "  , lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) as endtime_lag\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  --227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    ")\n",
    "select \n",
    "  label\n",
    ", starttime\n",
    ", endtime\n",
    ", endtime_lag\n",
    ", new_event_flag\n",
    ", rate, rateuom\n",
    ", statusdescription\n",
    "from t1\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note we have added the `endtime_lag` column to give a clearer idea of how the query is working. We can see the first row starts with `new_event_flag = 1` since `endtime_lag` is null. Next, the `endtime_lag != starttime`, so `new_event_flag` is again `= 1`.\n",
    "\n",
    "Finally, for row 2 (marked by **2** on the far left), the `endtime_lag == starttime` - and so `new_event_flag` is 0. This continues all the way until row **9**, where we can again see `endtime_lag != starttime`. Note that the `statusdescription` on row **8** even informs us why: it states that the administration has been \"Paused\". This is why we mentioned earlier that we were interested in the last row from an event.\n",
    "\n",
    "### Step 2: create a binary flag for new events\n",
    "\n",
    "With SQL, in order to aggregate groups of rows, we need a *partition*. That is, we need some key (usually an integer) which is unique for that set of rows. Once we have this unique key, we can do all the standard SQL aggregations like `max()`, `min()`, and so on (note: SQL \"window\" functions operate on the same principle, except you define the partition explicitly from a combination of columns).\n",
    "\n",
    "With this in mind, our next step is to use this flag to create a unique integer for each set of rows we'd like grouped. Since we'd like to group new events together, we can run a cumulative sum along `new_event_flag`: every time a new event occurs, the integer will increase and consequently that event will all have the same unique key. The code to do this is:\n",
    "\n",
    "```sql\n",
    "SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n",
    "```\n",
    "\n",
    "Let's see this in action:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from INPUTEVENTS for one patient with KCl...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>new_event_flag</th>\n",
       "      <th>time_partition</th>\n",
       "      <th>rate</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>statusdescription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 21:30</td>\n",
       "      <td>Day 12, 02:30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.002273</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9.997713</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.285715</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.112360</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.169492</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Paused</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9.997634</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.190549</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.040268</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.006060</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>Day 13, 16:29</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.005904</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 15:28</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4.007380</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.871428</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.905264</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.969388</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.906736</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.905759</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.904762</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.906250</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.907693</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>Day 16, 16:04</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.905613</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Durations from INPUTEVENTS for one patient with KCl...\")\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  --227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    ")\n",
    ", t2 as\n",
    "(\n",
    "    select \n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , new_event_flag\n",
    "    , SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    from t1\n",
    ")\n",
    "select \n",
    "      label\n",
    "    , starttime\n",
    "    , endtime\n",
    "    , new_event_flag\n",
    "    , time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "\n",
    "from t2\n",
    "order by starttime, endtime\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above (hopefully) makes it clear how a unique partition for each continuous segment of KCl administration can be delineated by cumulatively summing `new_event_flag` to create `time_partition`.\n",
    "\n",
    "### Step 3: create an integer to mark the last row of an event\n",
    "\n",
    "From above, it appears as though the *last* `statusdescription` would provide us useful debugging information as to why the administration event stopped - so we have another inline view where we create an integer which is 1 for the last `statusdescription`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from INPUTEVENTS for one patient with KCl...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>time_partition</th>\n",
       "      <th>rate</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>statusdescription</th>\n",
       "      <th>lastrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 21:30</td>\n",
       "      <td>Day 12, 02:30</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>2</td>\n",
       "      <td>10.002273</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>2</td>\n",
       "      <td>9.997713</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>2</td>\n",
       "      <td>10.285715</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>2</td>\n",
       "      <td>10.112360</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>2</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>2</td>\n",
       "      <td>10.169492</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>2</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>2</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Paused</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>3</td>\n",
       "      <td>9.997634</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>3</td>\n",
       "      <td>6.190549</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>3</td>\n",
       "      <td>6.040268</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>3</td>\n",
       "      <td>6.006060</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>Day 13, 16:29</td>\n",
       "      <td>3</td>\n",
       "      <td>6.005904</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>4</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 15:28</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>5</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>5</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>5</td>\n",
       "      <td>4.007380</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>5</td>\n",
       "      <td>5.871428</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>5</td>\n",
       "      <td>5.905264</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>5</td>\n",
       "      <td>5.969388</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>5</td>\n",
       "      <td>5.906736</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>5</td>\n",
       "      <td>5.905759</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>5</td>\n",
       "      <td>5.904762</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>5</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>5</td>\n",
       "      <td>5.906250</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>5</td>\n",
       "      <td>5.907693</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>5</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>5</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>5</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>5</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>Day 16, 16:04</td>\n",
       "      <td>5</td>\n",
       "      <td>5.905613</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Durations from INPUTEVENTS for one patient with KCl...\")\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  --227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    ")\n",
    ", t2 as\n",
    "(\n",
    "    select \n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    from t1\n",
    ")\n",
    ", t3 as\n",
    "(\n",
    "select\n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    , ROW_NUMBER() over (PARTITION BY icustay_id, label, time_partition order by starttime desc, endtime desc) as lastrow\n",
    "from t2\n",
    ")\n",
    "select \n",
    "label\n",
    ", starttime\n",
    ", endtime\n",
    ", time_partition\n",
    ", rate, rateuom\n",
    ", statusdescription\n",
    ", lastrow\n",
    " from t3\n",
    "order by starttime, endtime\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: aggregate to merge together contiguous start/end times\n",
    "\n",
    "Now we aggregate the `starttime` and `endtime` together by grouping by `time_partition`, as follows:\n",
    "\n",
    "* we want the first `starttime`, so we use `min(starttime)`\n",
    "* we want the last `endtime`, so we use `max(endtime)`\n",
    "* we want the `statusdescription` at the last row, so we aggregate a column where all rows except the last are null\n",
    "\n",
    "To give more detail on the last step, let's look at the SQL code:\n",
    "\n",
    "```sql\n",
    ", min(case when lastrow = 1 then statusdescription else null end) as statusdescription\n",
    "```\n",
    "\n",
    "Aggregate functions ignore null values, so if we set the column to null for all but `lastrow = 1`, then the aggregate function is guaranteed to only return the value at `lastrow = 1`. The use of aggregate function could be either `min()` or `max()` - since it only effectively operates on a single value.\n",
    "\n",
    "Tying it all together, we have the final query:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from INPUTEVENTS for one patient with KCl...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>rate_min</th>\n",
       "      <th>rate_max</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>statusdescription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 21:30</td>\n",
       "      <td>Day 12, 02:30</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>9.997713</td>\n",
       "      <td>10.285715</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Paused</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Calcium</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>1.201625</td>\n",
       "      <td>2.002708</td>\n",
       "      <td>grams/hour</td>\n",
       "      <td>Paused</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Calcium</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 13, 15:54</td>\n",
       "      <td>1.206690</td>\n",
       "      <td>1.805171</td>\n",
       "      <td>grams/hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 13, 16:29</td>\n",
       "      <td>6.005904</td>\n",
       "      <td>9.997634</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Calcium</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>1.602136</td>\n",
       "      <td>1.602136</td>\n",
       "      <td>grams/hour</td>\n",
       "      <td>Paused</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 15:28</td>\n",
       "      <td>Day 16, 16:04</td>\n",
       "      <td>4.007380</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Calcium</td>\n",
       "      <td>Day 14, 15:28</td>\n",
       "      <td>Day 16, 16:05</td>\n",
       "      <td>1.196013</td>\n",
       "      <td>1.990426</td>\n",
       "      <td>grams/hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Durations from INPUTEVENTS for one patient with KCl...\")\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    ")\n",
    ", t2 as\n",
    "(\n",
    "    select \n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    from t1\n",
    ")\n",
    ", t3 as\n",
    "(\n",
    "select\n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    , ROW_NUMBER() over (PARTITION BY icustay_id, label, time_partition order by starttime desc, endtime desc) as lastrow\n",
    "from t2\n",
    ")\n",
    "select\n",
    "  label\n",
    "  --, time_partition\n",
    "  , min(starttime) AS starttime\n",
    "  , max(endtime) AS endtime\n",
    "  , min(rate) AS rate_min\n",
    "  , max(rate) AS rate_max\n",
    "  , min(rateuom) AS rateuom\n",
    "  , min(case when lastrow = 1 then statusdescription else null end) as statusdescription\n",
    "from t3\n",
    "group by icustay_id, label, time_partition\n",
    "order by starttime, endtime\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above looks good - so we save the query to `query_inputevents` without the clause that isolates the data to one patient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query_inputevents = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    ")\n",
    ", t2 as\n",
    "(\n",
    "    select \n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    from t1\n",
    ")\n",
    ", t3 as\n",
    "(\n",
    "select\n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    , ROW_NUMBER() over (PARTITION BY icustay_id, label, time_partition order by starttime desc, endtime desc) as lastrow\n",
    "from t2\n",
    ")\n",
    "select\n",
    "    icustay_id\n",
    "    , time_partition as num\n",
    "    , min(starttime) AS starttime\n",
    "    , max(endtime) AS endtime\n",
    "    , label\n",
    "    --, min(rate) AS rate_min\n",
    "    --, max(rate) AS rate_max\n",
    "    --, min(rateuom) AS rateuom\n",
    "    --, min(case when lastrow = 1 then statusdescription else null end) as statusdescription\n",
    "from t3\n",
    "group by icustay_id, label, time_partition\n",
    "order by starttime, endtime\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "We now have a good method of combining contiguous events from `INPUTEVENTS_MV`. Note that this is *usually* not required, as the `linkorderid` is meant to partition these events for us. For example, lets look at a very common sedative agent used in the ICU, propofol:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from INPUTEVENTS for one patient given propofol...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>linkorderid</th>\n",
       "      <th>orderid</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>rate</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>amount</th>\n",
       "      <th>amountuom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>1405816</td>\n",
       "      <td>Day 09, 18:29</td>\n",
       "      <td>Day 10, 00:14</td>\n",
       "      <td>50.002502</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>17.250863</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>2101314</td>\n",
       "      <td>Day 10, 01:01</td>\n",
       "      <td>Day 10, 01:05</td>\n",
       "      <td>50.002502</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>0.200010</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>7312240</td>\n",
       "      <td>Day 10, 01:05</td>\n",
       "      <td>Day 10, 08:05</td>\n",
       "      <td>40.001221</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>16.800513</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>7169415</td>\n",
       "      <td>Day 10, 08:15</td>\n",
       "      <td>Day 10, 12:00</td>\n",
       "      <td>40.001221</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>9.000275</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>5852722</td>\n",
       "      <td>Day 10, 12:05</td>\n",
       "      <td>Day 10, 12:40</td>\n",
       "      <td>40.001221</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>1.400043</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>3365285</td>\n",
       "      <td>Day 10, 12:40</td>\n",
       "      <td>Day 10, 14:00</td>\n",
       "      <td>20.000627</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>1.600050</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>522225</td>\n",
       "      <td>522225</td>\n",
       "      <td>Day 10, 14:00</td>\n",
       "      <td>Day 10, 14:01</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>5245063</td>\n",
       "      <td>Day 10, 14:00</td>\n",
       "      <td>Day 10, 14:07</td>\n",
       "      <td>40.001254</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>0.280009</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>2703553</td>\n",
       "      <td>2703553</td>\n",
       "      <td>Day 10, 14:05</td>\n",
       "      <td>Day 10, 14:06</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>6687581</td>\n",
       "      <td>Day 10, 14:07</td>\n",
       "      <td>Day 11, 08:45</td>\n",
       "      <td>30.001253</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>33.541401</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>4912696</td>\n",
       "      <td>4912696</td>\n",
       "      <td>Day 10, 16:10</td>\n",
       "      <td>Day 10, 16:11</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>3838086</td>\n",
       "      <td>3838086</td>\n",
       "      <td>Day 10, 16:55</td>\n",
       "      <td>Day 10, 16:56</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>5665808</td>\n",
       "      <td>5665808</td>\n",
       "      <td>Day 11, 01:51</td>\n",
       "      <td>Day 11, 01:52</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>3755617</td>\n",
       "      <td>Day 11, 09:10</td>\n",
       "      <td>Day 11, 13:36</td>\n",
       "      <td>30.001253</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>7.980333</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Durations from INPUTEVENTS for one patient given propofol...\")\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , di.label\n",
    "  , mv.linkorderid, mv.orderid\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , rate, rateuom\n",
    "  , amount, amountuom\n",
    "from inputevents_mv mv\n",
    "inner join d_items di\n",
    "on mv.itemid = di.itemid\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    "and mv.itemid = 222168\n",
    ")\n",
    "select \n",
    "    label\n",
    "  , linkorderid, orderid\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , rate, rateuom\n",
    "  , amount, amountuom\n",
    "from t1\n",
    "order by starttime, endtime\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that `linkorderid` nicely delineates contiguous events without us having to put in the effort of above. It also separates *distinct* administrations. Above, at row **6**, we can see a \"1 minute\" delivery of propofol. This is how MetaVision tables (those which end in `_mv`) mark \"instant\" events - in the case of drug delivery, these are boluses of drugs administered to the patient. \n",
    "\n",
    "When using this data, we can group like events on a partition (as we did above), but we don't have to create the partition: it already exists with `linkorderid`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grouped durations from INPUTEVENTS for one patient given propofol...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>linkorderid</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>rate_min</th>\n",
       "      <th>rate_max</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>amount_min</th>\n",
       "      <th>amount_max</th>\n",
       "      <th>amountuom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>Day 09, 18:29</td>\n",
       "      <td>Day 11, 13:36</td>\n",
       "      <td>20.000627</td>\n",
       "      <td>50.002502</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>0.200010</td>\n",
       "      <td>33.541401</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>522225</td>\n",
       "      <td>Day 10, 14:00</td>\n",
       "      <td>Day 10, 14:01</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>2703553</td>\n",
       "      <td>Day 10, 14:05</td>\n",
       "      <td>Day 10, 14:06</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>4912696</td>\n",
       "      <td>Day 10, 16:10</td>\n",
       "      <td>Day 10, 16:11</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>3838086</td>\n",
       "      <td>Day 10, 16:55</td>\n",
       "      <td>Day 10, 16:56</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>5665808</td>\n",
       "      <td>Day 11, 01:51</td>\n",
       "      <td>Day 11, 01:52</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Grouped durations from INPUTEVENTS for one patient given propofol...\")\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , di.itemid, di.label\n",
    "  , mv.linkorderid, mv.orderid\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , amount, amountuom\n",
    "  , rate, rateuom\n",
    "from inputevents_mv mv\n",
    "inner join d_items di\n",
    "on mv.itemid = di.itemid\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    "and mv.itemid = 222168\n",
    ")\n",
    "select icustay_id\n",
    " , label\n",
    " , linkorderid\n",
    " , min(starttime) as starttime\n",
    " , max(endtime) as endtime\n",
    " , min(rate) as rate_min\n",
    " , max(rate) as rate_max\n",
    " , max(rateuom) as rateuom\n",
    " , min(amount) as amount_min\n",
    " , max(amount) as amount_max\n",
    " , max(amountuom) as amountuom\n",
    "from t1\n",
    "group by icustay_id, itemid, label, linkorderid\n",
    "order by starttime, endtime\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also worth noting that bolus administrations *do not have a `rate`*. They only have an `amount`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert CHARTEVENTS into durations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from CHARTEVENTS...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Day 11, 23:43</td>\n",
       "      <td>Day 12, 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Day 12, 22:00</td>\n",
       "      <td>Day 13, 16:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Day 14, 15:27</td>\n",
       "      <td>Day 16, 16:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convert CHARTEVENTS into durations\n",
    "# NOTE: we only look at a single patient as an exemplar\n",
    "print(\"Durations from CHARTEVENTS...\")\n",
    "query = query_schema + \"\"\"\n",
    "with crrt_settings as\n",
    "(\n",
    "select ce.icustay_id, ce.charttime\n",
    ", max(\n",
    "    case\n",
    "      when ce.itemid in\n",
    "      (\n",
    "        224149, -- Access Pressure\n",
    "        224144, -- Blood Flow (ml/min)\n",
    "        228004, -- Citrate (ACD-A)\n",
    "        225183, -- Current Goal\n",
    "        225977, -- Dialysate Fluid\n",
    "        224154, -- Dialysate Rate\n",
    "        224151, -- Effluent Pressure\n",
    "        224150, -- Filter Pressure\n",
    "        225958, -- Heparin Concentration (units/mL)\n",
    "        224145, -- Heparin Dose (per hour)\n",
    "        224191, -- Hourly Patient Fluid Removal\n",
    "        228005, -- PBP (Prefilter) Replacement Rate\n",
    "        228006, -- Post Filter Replacement Rate\n",
    "        225976, -- Replacement Fluid\n",
    "        224153, -- Replacement Rate\n",
    "        224152, -- Return Pressure\n",
    "        226457  -- Ultrafiltrate Output\n",
    "      ) then 1\n",
    "    else 0 end)\n",
    "    as RRT\n",
    "-- Below indicates that a new instance of CRRT has started\n",
    ", max(\n",
    "  case\n",
    "    -- System Integrity\n",
    "    when ce.itemid = 224146 and value in ('New Filter','Reinitiated')\n",
    "      then 1\n",
    "  else 0\n",
    " end ) as RRT_start\n",
    "-- Below indicates that the current instance of CRRT has ended\n",
    ", max(\n",
    "  case\n",
    "    -- System Integrity\n",
    "    when ce.itemid = 224146 and value in ('Discontinued','Recirculating')\n",
    "      then 1\n",
    "    when ce.itemid = 225956\n",
    "      then 1\n",
    "  else 0\n",
    " end ) as RRT_end\n",
    "from chartevents ce\n",
    "where ce.itemid in\n",
    "(\n",
    "  -- MetaVision ITEMIDs\n",
    "  -- Below require special handling\n",
    "  224146, -- System Integrity\n",
    "  225956,  -- Reason for CRRT Filter Change\n",
    "\n",
    "  -- Below are settings which indicate CRRT is started/continuing\n",
    "  224149, -- Access Pressure\n",
    "  224144, -- Blood Flow (ml/min)\n",
    "  228004, -- Citrate (ACD-A)\n",
    "  225183, -- Current Goal\n",
    "  225977, -- Dialysate Fluid\n",
    "  224154, -- Dialysate Rate\n",
    "  224151, -- Effluent Pressure\n",
    "  224150, -- Filter Pressure\n",
    "  225958, -- Heparin Concentration (units/mL)\n",
    "  224145, -- Heparin Dose (per hour)\n",
    "  224191, -- Hourly Patient Fluid Removal\n",
    "  228005, -- PBP (Prefilter) Replacement Rate\n",
    "  228006, -- Post Filter Replacement Rate\n",
    "  225976, -- Replacement Fluid\n",
    "  224153, -- Replacement Rate\n",
    "  224152, -- Return Pressure\n",
    "  226457  -- Ultrafiltrate Output\n",
    ")\n",
    "and ce.value is not null\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    "group by icustay_id, charttime\n",
    ")\n",
    "\n",
    "-- create the durations for each CRRT instance\n",
    "select icustay_id\n",
    "  , ROW_NUMBER() over (partition by icustay_id order by num) as num\n",
    "  , min(charttime) as starttime\n",
    "  , max(charttime) as endtime\n",
    "from\n",
    "(\n",
    "  select vd1.*\n",
    "  -- create a cumulative sum of the instances of new CRRT\n",
    "  -- this results in a monotonically increasing integer assigned to each CRRT\n",
    "  , case when RRT_start = 1 or RRT=1 or RRT_end = 1 then\n",
    "      SUM( NewCRRT )\n",
    "      OVER ( partition by icustay_id order by charttime )\n",
    "    else null end\n",
    "    as num\n",
    "  --- now we convert CHARTTIME of CRRT settings into durations\n",
    "  from ( -- vd1\n",
    "      select\n",
    "          icustay_id\n",
    "          -- this carries over the previous charttime\n",
    "          , case\n",
    "              when RRT=1 then\n",
    "                LAG(CHARTTIME, 1) OVER (partition by icustay_id, RRT order by charttime)\n",
    "              else null\n",
    "            end as charttime_lag\n",
    "          , charttime\n",
    "          , RRT\n",
    "          , RRT_start\n",
    "          , RRT_end\n",
    "          -- calculate the time since the last event\n",
    "          , case\n",
    "              -- non-null iff the current observation indicates settings are present\n",
    "              when RRT=1 then\n",
    "                CHARTTIME -\n",
    "                (\n",
    "                  LAG(CHARTTIME, 1) OVER\n",
    "                  (\n",
    "                    partition by icustay_id, RRT\n",
    "                    order by charttime\n",
    "                  )\n",
    "                )\n",
    "              else null\n",
    "            end as CRRT_duration\n",
    "\n",
    "          -- now we determine if the current event is a new instantiation\n",
    "          , case\n",
    "              when RRT_start = 1\n",
    "                then 1\n",
    "            -- if there is an end flag, we mark any subsequent event as new\n",
    "              when RRT_end = 1\n",
    "                -- note the end is *not* a new event, the *subsequent* row is\n",
    "                -- so here we output 0\n",
    "                then 0\n",
    "              when\n",
    "                LAG(RRT_end,1)\n",
    "                OVER\n",
    "                (\n",
    "                partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end\n",
    "                order by charttime\n",
    "                ) = 1\n",
    "                  then 1\n",
    "                -- if there is less than 2 hours between CRRT settings, we do not treat this as a new CRRT event\n",
    "              when (CHARTTIME - (LAG(CHARTTIME, 1)\n",
    "              OVER\n",
    "              (\n",
    "                partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end\n",
    "                order by charttime\n",
    "              ))) <= interval '2' hour\n",
    "                then 0\n",
    "            else 1\n",
    "          end as NewCRRT\n",
    "      -- use the temp table with only settings from chartevents\n",
    "      FROM crrt_settings\n",
    "  ) AS vd1\n",
    "  -- now we can isolate to just rows with settings\n",
    "  -- (before we had rows with start/end flags)\n",
    "  -- this removes any null values for NewCRRT\n",
    "  where\n",
    "    RRT_start = 1 or RRT = 1 or RRT_end = 1\n",
    ") AS vd2\n",
    "group by icustay_id, num\n",
    "having min(charttime) != max(charttime)\n",
    "order by icustay_id, num\n",
    "\"\"\"\n",
    "ce = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# happy with the above query - repeat it without the isolation to a single ICUSTAY_ID\n",
    "query_chartevents = query_schema + \"\"\"\n",
    "with crrt_settings as\n",
    "(\n",
    "select ce.icustay_id, ce.charttime\n",
    ", max(\n",
    "    case\n",
    "      when ce.itemid in\n",
    "      (\n",
    "        224149, -- Access Pressure\n",
    "        224144, -- Blood Flow (ml/min)\n",
    "        228004, -- Citrate (ACD-A)\n",
    "        225183, -- Current Goal\n",
    "        225977, -- Dialysate Fluid\n",
    "        224154, -- Dialysate Rate\n",
    "        224151, -- Effluent Pressure\n",
    "        224150, -- Filter Pressure\n",
    "        225958, -- Heparin Concentration (units/mL)\n",
    "        224145, -- Heparin Dose (per hour)\n",
    "        224191, -- Hourly Patient Fluid Removal\n",
    "        228005, -- PBP (Prefilter) Replacement Rate\n",
    "        228006, -- Post Filter Replacement Rate\n",
    "        225976, -- Replacement Fluid\n",
    "        224153, -- Replacement Rate\n",
    "        224152, -- Return Pressure\n",
    "        226457  -- Ultrafiltrate Output\n",
    "      ) then 1\n",
    "    else 0 end)\n",
    "    as RRT\n",
    "-- Below indicates that a new instance of CRRT has started\n",
    ", max(\n",
    "  case\n",
    "    -- System Integrity\n",
    "    when ce.itemid = 224146 and value in ('New Filter','Reinitiated')\n",
    "      then 1\n",
    "  else 0\n",
    " end ) as RRT_start\n",
    "-- Below indicates that the current instance of CRRT has ended\n",
    ", max(\n",
    "  case\n",
    "    -- System Integrity\n",
    "    when ce.itemid = 224146 and value in ('Discontinued','Recirculating')\n",
    "      then 1\n",
    "    when ce.itemid = 225956\n",
    "      then 1\n",
    "  else 0\n",
    " end ) as RRT_end\n",
    "from chartevents ce\n",
    "where ce.itemid in\n",
    "(\n",
    "  -- MetaVision ITEMIDs\n",
    "  -- Below require special handling\n",
    "  224146, -- System Integrity\n",
    "  225956,  -- Reason for CRRT Filter Change\n",
    "\n",
    "  -- Below are settings which indicate CRRT is started/continuing\n",
    "  224149, -- Access Pressure\n",
    "  224144, -- Blood Flow (ml/min)\n",
    "  228004, -- Citrate (ACD-A)\n",
    "  225183, -- Current Goal\n",
    "  225977, -- Dialysate Fluid\n",
    "  224154, -- Dialysate Rate\n",
    "  224151, -- Effluent Pressure\n",
    "  224150, -- Filter Pressure\n",
    "  225958, -- Heparin Concentration (units/mL)\n",
    "  224145, -- Heparin Dose (per hour)\n",
    "  224191, -- Hourly Patient Fluid Removal\n",
    "  228005, -- PBP (Prefilter) Replacement Rate\n",
    "  228006, -- Post Filter Replacement Rate\n",
    "  225976, -- Replacement Fluid\n",
    "  224153, -- Replacement Rate\n",
    "  224152, -- Return Pressure\n",
    "  226457  -- Ultrafiltrate Output\n",
    ")\n",
    "and ce.value is not null\n",
    "group by icustay_id, charttime\n",
    ")\n",
    "\n",
    "-- create the durations for each CRRT instance\n",
    "select icustay_id\n",
    "  , ROW_NUMBER() over (partition by icustay_id order by num) as num\n",
    "  , min(charttime) as starttime\n",
    "  , max(charttime) as endtime\n",
    "from\n",
    "(\n",
    "  select vd1.*\n",
    "  -- create a cumulative sum of the instances of new CRRT\n",
    "  -- this results in a monotonically increasing integer assigned to each CRRT\n",
    "  , case when RRT_start = 1 or RRT=1 or RRT_end = 1 then\n",
    "      SUM( NewCRRT )\n",
    "      OVER ( partition by icustay_id order by charttime )\n",
    "    else null end\n",
    "    as num\n",
    "  --- now we convert CHARTTIME of CRRT settings into durations\n",
    "  from ( -- vd1\n",
    "      select\n",
    "          icustay_id\n",
    "          -- this carries over the previous charttime\n",
    "          , case\n",
    "              when RRT=1 then\n",
    "                LAG(CHARTTIME, 1) OVER (partition by icustay_id, RRT order by charttime)\n",
    "              else null\n",
    "            end as charttime_lag\n",
    "          , charttime\n",
    "          , RRT\n",
    "          , RRT_start\n",
    "          , RRT_end\n",
    "          -- calculate the time since the last event\n",
    "          , case\n",
    "              -- non-null iff the current observation indicates settings are present\n",
    "              when RRT=1 then\n",
    "                CHARTTIME -\n",
    "                (\n",
    "                  LAG(CHARTTIME, 1) OVER\n",
    "                  (\n",
    "                    partition by icustay_id, RRT\n",
    "                    order by charttime\n",
    "                  )\n",
    "                )\n",
    "              else null\n",
    "            end as CRRT_duration\n",
    "\n",
    "          -- now we determine if the current event is a new instantiation\n",
    "          , case\n",
    "              when RRT_start = 1\n",
    "                then 1\n",
    "            -- if there is an end flag, we mark any subsequent event as new\n",
    "              when RRT_end = 1\n",
    "                -- note the end is *not* a new event, the *subsequent* row is\n",
    "                -- so here we output 0\n",
    "                then 0\n",
    "              when\n",
    "                LAG(RRT_end,1)\n",
    "                OVER\n",
    "                (\n",
    "                partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end\n",
    "                order by charttime\n",
    "                ) = 1\n",
    "                  then 1\n",
    "                -- if there is less than 2 hours between CRRT settings, we do not treat this as a new CRRT event\n",
    "              when (CHARTTIME - (LAG(CHARTTIME, 1)\n",
    "              OVER\n",
    "              (\n",
    "                partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end\n",
    "                order by charttime\n",
    "              ))) <= interval '2' hour\n",
    "                then 0\n",
    "            else 1\n",
    "          end as NewCRRT\n",
    "      -- use the temp table with only settings from chartevents\n",
    "      FROM crrt_settings\n",
    "  ) AS vd1\n",
    "  -- now we can isolate to just rows with settings\n",
    "  -- (before we had rows with start/end flags)\n",
    "  -- this removes any null values for NewCRRT\n",
    "  where\n",
    "    RRT_start = 1 or RRT = 1 or RRT_end = 1\n",
    ") AS vd2\n",
    "group by icustay_id, num\n",
    "having min(charttime) != max(charttime)\n",
    "order by icustay_id, num\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract durations from PROCEDUREEVENTS_MV\n",
    "\n",
    "PROCEDUREEVENTS_MV contains entries for dialysis. As a reminder from the above, we picked the following `itemid`:\n",
    "\n",
    "* 225802 -- Dialysis - CRRT\n",
    "* 225803 -- Dialysis - CVVHD\n",
    "* 225809 -- Dialysis - CVVHDF\n",
    "* 225955 -- Dialysis - SCUF\n",
    "\n",
    "Extracting data for these entries is straightforward. Each instance of CRRT is documented with a single `starttime` and a single `stoptime`, with no need to merge together different rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from PROCEDUREEVENTS_MV...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Day 14, 15:27</td>\n",
       "      <td>Day 16, 16:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract the durations from PROCEDUREEVENTS_MV\n",
    "# NOTE: we only look at a single patient as an exemplar\n",
    "print(\"Durations from PROCEDUREEVENTS_MV...\")\n",
    "query = query_schema + \"\"\"\n",
    "select icustay_id\n",
    "  , ROW_NUMBER() over (partition by icustay_id order by starttime, endtime) as num\n",
    "  , starttime, endtime\n",
    "from procedureevents_mv\n",
    "where itemid in\n",
    "(\n",
    "    225802 -- Dialysis - CRRT\n",
    "  , 225803 -- Dialysis - CVVHD\n",
    "  , 225809 -- Dialysis - CVVHDF\n",
    "  , 225955 -- Dialysis - SCUF\n",
    ")\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    "order by icustay_id, num\n",
    "\"\"\"\n",
    "pe = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(pe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the above documentation is quite dilligent: the entry pauses between the first and second row for 1 hour representing an actual pause in the administration of CRRT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# happy with above query\n",
    "query_procedureevents = query_schema + \"\"\"\n",
    "select icustay_id\n",
    "  , ROW_NUMBER() over (partition by icustay_id order by starttime, endtime) as num\n",
    "  , starttime, endtime\n",
    "from procedureevents_mv\n",
    "where itemid in\n",
    "(\n",
    "    225802 -- Dialysis - CRRT\n",
    "  , 225803 -- Dialysis - CVVHD\n",
    "  , 225809 -- Dialysis - CVVHDF\n",
    "  , 225955 -- Dialysis - SCUF\n",
    ")\n",
    "order by icustay_id, num\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Roundup: data from INPUTEVENTS_MV, CHARTEVENTS, and PROCEDUREEVENTS_MV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(\"Durations from INPUTEVENTS...\")\n",
    "ie = pd.read_sql_query(query_inputevents,con)\n",
    "\n",
    "print(\"Durations from CHARTEVENTS...\")\n",
    "ce = pd.read_sql_query(query_chartevents,con)\n",
    "\n",
    "print(\"Durations from PROCEDUREEVENTS...\")\n",
    "pe = pd.read_sql_query(query_procedureevents,con)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare durations\n",
    "\n",
    "We now need to merge together the above durations into a single, master set of CRRT administrations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Day 09, 18:10</td>\n",
       "      <td>Day 13, 15:44</td>\n",
       "      <td>inputevents_ca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1</td>\n",
       "      <td>Day 09, 18:00</td>\n",
       "      <td>Day 12, 15:15</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2</td>\n",
       "      <td>Day 12, 16:02</td>\n",
       "      <td>Day 12, 19:01</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>3</td>\n",
       "      <td>Day 12, 21:00</td>\n",
       "      <td>Day 13, 14:03</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1</td>\n",
       "      <td>Day 09, 18:00</td>\n",
       "      <td>Day 13, 15:04</td>\n",
       "      <td>procedureevents</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAJaCAYAAADXkuE+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBQAAAAAYQykFAAAAABjzP2CcIsuK88HEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe95c27de50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how many PROCEDUREEVENTS_MV dialysis events encapsulate CHARTEVENTS/INPUTEVENTS_MV?\n",
    "# vice-versa?\n",
    "iid = 205508\n",
    "\n",
    "# compare the above durations\n",
    "ce['source'] = 'chartevents'\n",
    "ie['source'] = 'inputevents_kcl'\n",
    "ie.loc[ie['label']=='Calcium','source'] = 'inputevents_ca' \n",
    "pe['source'] = 'procedureevents'\n",
    "df = pd.concat([ie[['icustay_id','num','starttime','endtime','source']], ce, pe])\n",
    "idxDisplay = df['icustay_id'] == iid\n",
    "display_df(df.loc[idxDisplay, :])\n",
    "\n",
    "# 2) how many have no overlap whatsoever?\n",
    "\n",
    "\n",
    "col_dict = {'chartevents': [247,129,191],\n",
    "           'inputevents_kcl': [255,127,0],\n",
    "           'inputevents_ca': [228,26,28],\n",
    "           'procedureevents': [55,126,184]}\n",
    "\n",
    "for c in col_dict:\n",
    "    col_dict[c] = [x/256.0 for x in col_dict[c]]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=[16,10])\n",
    "m = 0.\n",
    "M = np.sum(idxDisplay)\n",
    "\n",
    "# dummy plots for legend\n",
    "legend_handle = list()\n",
    "for c in col_dict:\n",
    "    legend_handle.append(mlines.Line2D([], [], color=col_dict[c], marker='o',\n",
    "                              markersize=15, label=c))\n",
    "\n",
    "for row in df.loc[idxDisplay,:].iterrows():\n",
    "    # row is a tuple: [index, actual_data], so we use row[1]\n",
    "    plt.plot([row[1]['starttime'].to_pydatetime(), row[1]['endtime'].to_pydatetime()], [0+m/M,0+m/M],\n",
    "            'o-',color=col_dict[row[1]['source']],\n",
    "            markersize=15, linewidth=2)\n",
    "    m=m+1\n",
    "    \n",
    "ax.xaxis.set_minor_locator(dates.HourLocator(byhour=[0,12],interval=1))\n",
    "ax.xaxis.set_minor_formatter(dates.DateFormatter('%H:%M'))\n",
    "ax.xaxis.grid(True, which=\"minor\")\n",
    "ax.xaxis.set_major_locator(dates.DayLocator(interval=1))\n",
    "ax.xaxis.set_major_formatter(dates.DateFormatter('\\n%d\\n%a'))\n",
    "\n",
    "ax.set_ylim([-0.1,1.0])\n",
    "\n",
    "plt.legend(handles=legend_handle,loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7AAAAJOCAYAAACOUusfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9547b4290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAJOCAYAAABP1aGzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAA4hlAKAAAAAHAMoRQAAAAA4Jj/BVwJczs0EfLsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe95c03b4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAJOCAYAAABP1aGzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe95c25d290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA68AAAJOCAYAAABY9zF6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAYD2aVwAAAACA9f4fzwRX6t0qT4sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe94c05bb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAJOCAYAAABP1aGzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKQUAAAAAeIamFAAAAADgmf8Hks6Ov1lZ/6gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe94c05bb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6cAAAJOCAYAAABLIHGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBBC6O7/A9OYywy59MrxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9546cb6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAJOCAYAAABP1aGzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAIDAEEoBAAAAAIH5/5BNxovkh7vnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe95401f890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6YAAAJOCAYAAACk4hqwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe94c14edd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAJOCAYAAABP1aGzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXiODUiGEEEIIIYQQXvP/AdYMFmS+u8i2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9542e9110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6wAAAJOCAYAAACzwIp5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsAIAAFBK/w/plLwxxTQIwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe957f35ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print out the above for 10 examples\n",
    "\n",
    "# compare the above durations\n",
    "ce['source'] = 'chartevents'\n",
    "ie['source'] = 'inputevents_kcl'\n",
    "ie.loc[ie['label']=='Calcium','source'] = 'inputevents_ca' \n",
    "pe['source'] = 'procedureevents'\n",
    "df = pd.concat([ie[['icustay_id','num','starttime','endtime','source']], ce, pe])\n",
    "\n",
    "for iid in np.sort(df.icustay_id.unique()[0:10]):\n",
    "    iid = int(iid)\n",
    "    # how many PROCEDUREEVENTS_MV dialysis events encapsulate CHARTEVENTS/INPUTEVENTS_MV?\n",
    "    # vice-versa?\n",
    "    idxDisplay = df['icustay_id'] == iid\n",
    "    \n",
    "    # no need to display here\n",
    "    #display_df(df.loc[idxDisplay, :])\n",
    "    \n",
    "    # 2) how many have no overlap whatsoever?\n",
    "    col_dict = {'chartevents': [247,129,191],\n",
    "               'inputevents_kcl': [255,127,0],\n",
    "               'inputevents_ca': [228,26,28],\n",
    "               'procedureevents': [55,126,184]}\n",
    "\n",
    "    for c in col_dict:\n",
    "        col_dict[c] = [x/256.0 for x in col_dict[c]]\n",
    "\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=[16,10])\n",
    "    m = 0.\n",
    "    M = np.sum(idxDisplay)\n",
    "\n",
    "    # dummy plots for legend\n",
    "    legend_handle = list()\n",
    "    for c in col_dict:\n",
    "        legend_handle.append(mlines.Line2D([], [], color=col_dict[c], marker='o',\n",
    "                                  markersize=15, label=c))\n",
    "\n",
    "    for row in df.loc[idxDisplay,:].iterrows():\n",
    "        # row is a tuple: [index, actual_data], so we use row[1]\n",
    "        plt.plot([row[1]['starttime'].to_pydatetime(), row[1]['endtime'].to_pydatetime()], [0+m/M,0+m/M],\n",
    "                'o-',color=col_dict[row[1]['source']],\n",
    "                markersize=15, linewidth=2)\n",
    "        m=m+1\n",
    "\n",
    "    ax.xaxis.set_minor_locator(dates.HourLocator(byhour=[0,6,12,18],interval=1))\n",
    "    ax.xaxis.set_minor_formatter(dates.DateFormatter('%H:%M'))\n",
    "    ax.xaxis.grid(True, which=\"minor\")\n",
    "    ax.xaxis.set_major_locator(dates.DayLocator(interval=1))\n",
    "    ax.xaxis.set_major_formatter(dates.DateFormatter('\\n%d-%m-%Y'))\n",
    "\n",
    "    ax.set_ylim([-0.1,1.0])\n",
    "\n",
    "    plt.legend(handles=legend_handle,loc='best')\n",
    "    \n",
    "    # if you want to save the figures, uncomment the line below\n",
    "    #plt.savefig('crrt_' + str(iid) + '.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the plots, it appears that durations from INPUTEVENTS and PROCEDUREEVENTS_MV are redundant to that in CHARTEVENTS. Furthermore, the durations from CHARTEVENTS appear to better reflect small interruptions due to clotted lines and paused treatment. As a result, we only use the durations from the CHARTEVENTS. The `concepts/durations/crrt-durations.sql` query contains the final duration query which additionally has `itemid` from CareVue. These `itemid` were determined in a similar manner to the above."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
